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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Characterization of self-magnetic pinch radiographic diode performance on RITS-6 at Sandia National Laboratories. II. Coupling between the inductive voltage adder and the SMP load

The self-magnetic pinch (SMP) diode is a type of radiographic diode used to generate an intense electron beam for radiographic applications. At Sandia National Laboratories, SMP was the diode load for the six-cavity radiographic integrated test stand inductive voltage adder (IVA) driver operated in a magnetically insulated transmission line (MITL). The MITL contributes a flow current in addition to the current generated within the diode itself. Extensive experiments with a MITL of 40Ω load impedance [T. J. Renk et al., Phys. Plasmas 29, 023105 (2022)] indicate that the additional flow current leads to results similar to what might be expected from a conventional high-voltage interface driver, where flow current is not present. However, when the MITL flow impedance was increased to 80 Ω, qualitatively different diode behavior was observed. This includes large retrapping waves suggestive of an initial coupling to low impedance as well as diode current decreasing with time even as the total current does not. A key observation is that the driver generates total current (flow + diode) consistent with the flow impedance of the MITL used. The case is made in this paper that the 80Ω MITL experiments detailed here can only be understood when the IVA-MITL-SMP diode is considered as a total system. Additionally, the constraint of fixed total current plus the relatively high flow impedance limits the ability of the diode (whether SMP or other type) to act as an independent load. An unexpected new result is that in tracking the behavior of the electron strike angle on the converter as a function of time, we observed that the conventional cIV x “Radiographic” radiation scaling (where x~2.2) begins to break down for voltages above 8 MV, and cubic scaling is required to recover accurate angle tracking.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Attractive Noncovalent Interactions versus Steric Confinement in Asymmetric Supramolecular Catalysis

The remarkable catalytic performance of enzymes stems from their ability to engage in precise noncovalent interactions (NCIs) within a sterically confined space. Supramolecular catalysis seeks to emulate and understand these strategies through the rational design of simple and controlled catalyst microenvironments. While both steric confinement and attractive interactions have been invoked as key to host activity, their relative contribution to rate enhancement and selectivity, as well as potential trade-offs, remains an outstanding question. Here, we address this question by systematically comparing two metal–organic supramolecular catalysts, which differ in the strength of their attractive noncovalent interactions and in their cavity volume. Our findings reveal that the catalyst with the larger cavity, and with stronger available NCIs, exhibits both significant rate acceleration (100-fold) and enhanced enantioselectivity (84% vs 14% ee) in a model ketone reduction compared to its smaller analogue. Mechanistic analysis, binding competition experiments, and computational modeling indicate that these differences predominantly stem from stabilizing noncovalent interactions in the larger catalyst, a result that challenges existing steric-based models of supramolecular stereoinduction. Understanding the governing factors of asymmetric induction and rate acceleration in supramolecular hosts will undoubtedly inform future catalyst design.

Catalysts↗
DOE OSTI2021

Quasar clustering at redshift 6

Large-scale surveys over the last years have revealed about 300 quasi-stellar objects (QSOs) at redshifts above 6. Follow-up observations have identified surprising properties, such as the very high black hole (BH) masses, spatial correlations with surrounding cold gas of the host galaxy, and high CIV-MgII Velocity shifts. In particular, the discovery of luminous high-redshift quasars suggests that at least some BHs likely have high masses at birth and grow efficiently. Our aim is to quantify quasar pairs at high redshift for a large sample of objects. This provides a new key constraint on a combination of parameters related to the origin and assembly for the most massive BHs: formation efficiency and clustering, growth efficiency, and the relative contribution of BH mergers. We observed 116 spectroscopically confirmed QSOs around redshift 6 with the simultaneous seven-channel imager Gamma-ray Burst Optical/Near-infrared Detector in order to search for companions. Applying colour-colour cuts identical to those which led to the spectroscopically confirmed QSOs, we performed Le PHARE fits to the 26 best QSO pair candidates, and obtained spectroscopic observations for 11 of them. We do not find any QSO pair with a companion brighter than M 1450 (AB) < –26 mag within our 0.1–3.3 h –1 cMpc search radius, in contrast to the serendipitous findings in the redshift range 4–5. However, a small fraction of such pairs at this luminosity and redshift is consistent with indications from present-day cosmological-scale galaxy evolution models. In turn, the incidence of L- and T-type brown dwarfs, which occupy a similar colour space to z ~ 6 QSOs, is higher than expected, by a factor of 5 and 20, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Hybrid simulations of sub-cyclotron compressional and global Alfvén eigenmode stability in spherical tokamaks

A comprehensive numerical study has been conducted in order to investigate the stability of beam-driven, sub-cyclotron-frequency compressional Alfvén eigenmodes (CAEs) and global Alfvén eigenmodes (GAEs) in low-aspect-ratio plasmas for a wide range of beam parameters. The presence of CAEs and GAEs has previously been linked to anomalous electron temperature profile flattening at high beam powers in NSTX experiments, prompting a further examination of the conditions necessary for their excitation. Linear simulations have been performed with the hybrid MHD–kinetic initial value code HYM in order to capture the general Doppler-shifted cyclotron resonance that drives the modes. Additionally, three distinct types of modes were found in the simulations—co-CAEs, cntr-GAEs, and co-GAEs—with differing spectral and stability properties. The simulations revealed that unstable GAEs are more ubiquitous than unstable CAEs, which is consistent with experimental observations, as they are excited at lower beam energies and generally have larger growth rates. Local analytic theory is used to explain key features of the simulation results, including the preferential excitation of different modes based on beam injection geometry and the growth rate dependence on the beam injection velocity, critical velocity, and degree of velocity space anisotropy. The background damping rate is inferred from the simulations and analytically estimated for relevant sources absent from the simulation model, indicating that co-CAEs are closer to marginal stability than modes driven by the cyclotron resonances.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Tailoring the Mass Transport to Achieve High-Performing Unitized Regenerative Fuel Cells Considering Practical Operating Conditions

Unitized regenerative fuel cells (URFCs) convert electrical energy to chemical bonds in hydrogen during charge and convert chemical energy to output electricity during discharge, offering a promising solution to long-term energy storage. Recent studies indicate that the round-trip-voltaic efficiency (RTE) and longevity of URFCs are limited by complex mass transport during charging and discharging. Here, we first investigate how different porous transport layer (PTL) structures can impact URFC performance. The preferred PTL has a low tortuosity and high porosity, leading to a high RTE above 50% at 1 A cm −2 using Nafion 212. Moreover, thicker membranes, such as Solvay 90, are required to ensure mechanical stability and minimize H 2 crossover when operating under high differential pressure. Although this assembly inevitably leads to a higher ohmic loss, the RTE can be improved by further tailoring the electrode structures to facilitate mass transport by using supported catalyst, which still achieves over 50% RTEs at 1 A cm −2 . Optimization of porous structure to mitigate mass transport resistance with appropriate materials down selection considering practical application requirements can be a key design principle for achieving high-performing URFCs.

25 ENERGY STORAGE↗
DOE OSTI2025

Thermomechanical Modeling and Analysis of a High-Temperature Light Trapping Planar Cavity Receiver

Solar energy harnessed through concentrating solar power (CSP) systems offers a promising path to sustainable energy production, with the efficiency and longevity of these systems relying on key components like solar receivers. This study analyzes the thermomechanical behavior of an innovative enclosed light-trapping solar receiver optimized for particle heating applications. The receiver utilizes sheet metal alloys to form enclosed cavities that reflect and trap incoming solar flux, as well as enclosed channels that contain fluidized particle beds absorbing solar heat. Finite element analysis (FEA) is applied to predict the receiver's thermomechanical performance under extreme solar flux conditions. Temperature distributions from a thermal model simulating a multi-panel assembly at steady state are input into the FEA thermomechanical model for stress analysis. A key aspect of the analysis focuses on evaluating creep-fatigue damage, with a design target of achieving a 30- year service life. Various stress relief techniques are also proposed to extend the receiver's service life. The results highlight the significant impact of the particle-to-wall heat transfer coefficients (HTCs), ranging from 800 W/m2*K to 1400 W/m2*K. The 800 W/m2*K case shows a maximum von Mises stress of 164 MPa, while the 1400 W/m2*K case reduces it to 150 MPa. The creep life increases from 4,000 hrs in the 800 W/m2*K case to over 100,000 hrs in the 1400 W/m2*K case with Inconel 740H used, indicating that higher HTCs reduce stress and extend lifespan. This research advances the design of high-efficiency, low-stress solar receivers for particle-based thermal energy storage in CSP and industrial heating applications.

concentrating solar power↗
DOE OSTI2023

Exploiting electricity market dynamics using flexible electrolysis units for retrofitting methanol synthesis

Here we investigate the economic viability of integrating flexible electrolysis units to produce hydrogen in methanol synthesis processes. Specifically, we investigate whether this approach can help reduce methanol production costs by strategically exploiting dynamics of electricity markets. Our study integrates high-fidelity process simulations, optimization tools, and microkinetic modeling (informed by density functional theory) to conduct detailed techno-economic analyses and to compare performance against traditional processes that use hydrogen produced via steam-methane reforming (SMR). We also use this approach to estimate the levelized cost of hydrogen (LCOH) as a function of time-varying electricity prices (from day-ahead and real-time prices) and of key techno-economic parameters. Our results show that the proposed electrification framework is cost-competitive under certain electricity market conditions. Specifically, we find that, when the electrolysis system is operated in flexible mode (and can respond to dynamics of electricity markets), the associated electricity cost nearly collapses to zero. Conversely, when the unit is not flexible (and cannot respond to markets), the electricity cost comprises 60% of the total cost. Our results also reveal that the LCOH of the flexible electrolysis system participating in real-time electricity markets is 31% lower than the LCOH obtained from SMR. Overall, this indicates that exploiting the dynamics of electricity markets can make hydrogen production cost-competitive and this can lead to viable alternatives to electrify methanol production and other hydrogen-based processes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Electrolyte Oxidation Pathways in Lithium-Ion Batteries

The mitigation of decomposition reactions of lithium-ion battery electrolyte solutions is of critical importance in controlling device lifetime and performance. However, due to the complexity of the system, exacerbated by the diverse set of electrolyte compositions, electrode materials, and operating parameters, a clear understanding of the key chemical mechanisms remains elusive. In this work, operando pressure measurements, solution NMR, and electrochemical methods were combined to study electrolyte oxidation and reduction at multiple cell voltages. Two-compartment LiCoO 2 /Li cells were cycled with a lithium-ion conducting glass–ceramic separator so that the species formed at each electrode could be identified separately and further reactions of these species at the opposite electrode prevented. One principal finding is that chemical oxidation (with an onset voltage of ~4.7 V vs Li/Li + for LiCoO 2 ), rather than electrochemical reaction, is the dominant decomposition process at the positive electrode surface in this system. This is ascribed to the well-known release of reactive oxygen at higher states-of-charge, indicating that reactions of the electrolyte at the positive electrode are intrinsically linked to surface reactivity of the active material. Soluble electrolyte decomposition products formed at both electrodes are characterized, and a detailed reaction scheme is constructed to rationalize the formation of the observed species. The insights on electrolyte decomposition through reactions with reactive oxygen species identified through this work have a direct impact on understanding and mitigating degradation in high-voltage/higher-energy-density LiCoO 2 -based cells, and more generally for cells containing nickel-containing cathode materials (e.g., LiNi x Mn y Co z O 2 ; NMCs), as they lose oxygen at lower operating voltages.

25 ENERGY STORAGE↗
DOE OSTI2024

Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

Nazaralizadeh, Solmaz (ORCID:0009000737004039)↗
DOE OSTI2025

Reassessing the Origins and Contemporary Relevance of ck Acceptability Parameters: Evolving Perspectives on Similarity

“Sensitivity and Uncertainty Analyses Applied to Criticality Safety Validation,” introduces sensitivity and uncertainty methods to address challenges in defining and extending areas of applicability for criticality safety validation. These areas are traditionally defined by the bounds or limits on key parameters, but establishing valid ranges and managing complex parameter variations remain challenging. NUREG/CR-6655 introduces ck and other integral indices, as well as concepts such as the completeness of benchmark coverage, to better quantify system similarities. The work proposed herein seeks to evaluate these foundational concepts to ensure that the bounds remain effective in guiding the assessment of similarity and applicability in modern applications. The concept of completeness, along with other parameters envisioned within the framework, serves as an example of the foundational ideas that have been established, though their effectiveness in practice may not be fully understood. Advancements in scripting tools, coupled with the speed and efficiency of modern computing and statistical models, now allow for faster and more thorough assessments than previously possible. These advancements also enable the identification of trends within the data, which could provide additional insight into system behavior and further broaden the scope of previously performed benchmarks. By leveraging these capabilities, we will revisit and expand the scope of these foundational methods to determine whether the necessary elements for robust similarity evaluation are already embedded, partially realized, or remain untapped.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗
DOE OSTI2025

Recyclable Design for Retaining High Solar Absorptivity of the Media in CSP

Efficient thermal energy storage is pivotal to lowering the levelized cost of electricity (LCOE) for Concentrating Solar Power (CSP) plants. In solid-particle systems, however, prolonged high-temperature service degrades particle solar absorptivity, eroding overall efficiency. This project demonstrates a hydrogen-assisted recovery process that reliably restores absorptivity to >90 %, offering a practical route to sustain long-term CSP performance. Bench-scale investigations mapped the reduction kinetics of optically faded particles across hydrogen concentrations, temperatures, and residence times. Coupling mass-spectrometric monitoring with machine-learning optimization minimized energy demand while maximizing absorptivity gain. The resulting process window—moderate hydrogen partial pressures, 15–30 min dwell times, and temperatures well below initial calcination levels—cuts energy consumption well below that of incumbent re-blackening methods. A prototype recovery reactor processed multiple 2 kg batches with repeatable outcomes, confirming scalability and operational robustness. Integrated techno-economic analysis indicates material and operating cost reductions exceeding 15 % relative to conventional particle replacement or chemical re-coating, translating directly into lower LCOE for next-generation CSP facilities. By uniting fundamental reaction-kinetics insight with pragmatic engineering, this work advances the solid-particle pathway, delivering a cost-effective, field-deployable solution to one of CSP’s key durability challenges and strengthening the commercial outlook for high-temperature renewable power.

14 SOLAR ENERGY↗
DOE OSTI2024

High–Performance NiCo 2 O 4 /Graphene Quantum Dots for Asymmetric and Symmetric Supercapacitors with Enhanced Energy Efficiency

For the sustainable growth of future generations, energy storage technologies like supercapacitors and batteries are becoming more and more common. However, reliable and high-performance materials’ design and development is the key for the widespread adoption of batteries and supercapacitors. Quantum dots with fascinating and unusual properties are expected to revolutionize future technologies. However, while the recent discovery of quantum dots honored with a Nobel prize in Chemistry, their benefits for the tenacious problem of energy are not realized yet. In this context, herein, chemical-composition tuning enabled exceptional performance of NiCo 2 O 4 (NCO)/graphene quantum dots (GQDs) is reported, which outperform the existing similar materials, in supercapacitors. A comprehensive study is performed on the synthesis, characterization, and electrochemical performance evaluation of highly functional NCO/GQDs in supercapacitors delivering enhanced energy efficiency. The high-performance, functional NCO/GQDs electrode materials are synthesized by the incorporation of GQDs into NCO. The effect of variable amount of GQDs on the energy performance characteristics of NCO/GQDs in supercapacitors is studied systematically. In-depth structural and chemical bonding analyses using X-ray diffraction (XRD) and Raman spectroscopic studies indicate that all the NCO/GQDs composites crystallize in the spinel cubic phase of NiCo 2 O 4 while graphene integration evident in all the NCO/GQDs. The scanning electron microscopy imaging analysis reveals homogeneously distributed spherical particles with a size distribution of 5–9 nm validating the formation of QDs. The high-resolution transmission electron microscopy analyses reveal that the NCOQDs are anchored on graphene sheets, which provide a high surface area of 42.27 m 2 g –1 and high mesoporosity for the composition of NCO/GQDs-10%. In addition to establishing reliable electrical connection to graphene sheets, the NCOQDs provide reliable 3D-conductive channels for rapid transport throughout the electrode as well as synergistic effects. Chemical-composition tuning, and optimization yields NCO/GQDs-10% to deliver the best specific capacitance of 3940 Fg –1 at 0.5 Ag –1 , where the electrodes retain ≈98% capacitance after 5000 cycles. The NCO/GQD-10%//AC asymmetric supercapacitor device demonstrates outstanding energy density and power density values of 118.04 Wh kg –1 and 798.76 W kg –1 , respectively. The NCO/GQDs-10%//NCO/GQDs-10% symmetric supercapacitor device delivers excellent energy and power density of 24.30 Wh kg –1 and 500 W kg –1 , respectively. These results demonstrate and conclude that NCO/GQDs are exceptional and prospective candidates for developing next-generation high-performance and sustainable energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2025

The Role of Water Volume Fraction on Water Adsorption in Anion Exchange Membranes

Water absorption plays a key role in the performance of polymeric anion exchange membranes. It influences important properties such as ionic conductivity and mechanical strength and alters their performance as solid electrolytes in hydrogen electrochemical devices for energy conversion. However, computational approaches that address the relationship between the polymer design and the absorption process are scarce. In this work, we introduce a simple thermodynamic model to predict the water absorption isotherms of polyelectrolyte membranes in contact with a water vapor reservoir that incorporates the specific chemical design of the polymers. The model accurately predicts the water content and macrostructural properties of polynorbornene membranes as a function of the water activity and successfully captures the effect of various polymer design parameters. The energy of pairwise attractive interactions predicted by our model provides a means to interpret the absorption process at the molecular level. The model also reveals the most significant favorable and unfavorable contributions to the free energy and indicates that their balance is solely governed by the water volume fraction, regardless of the polymer design. This universal behavior leads to important implications in the search for better ion exchange membranes.

Zaldivar, Gervasio [New York University, Brooklyn,↗
DOE OSTI2022

High performance polymerized small molecule acceptor by synergistic optimization on $\pi$-bridge linker and side chain

The polymerized small-molecule acceptors have attracted great attention for application as polymer acceptor in all-polymer solar cells recently. The modification of small molecule acceptor building block and the π-bridge linker is an effective strategy to improve the photovoltaic performance of the polymer acceptors. In this work, we synthesized a new polymer acceptor PG-IT2F which is a modification of the representative polymer acceptor PY-IT by replacing its upper linear alkyl side chains on the small molecule building block with branched alkyl chains and attaching difluorene substituents on its thiophene π-bridge linker. Through this synergistic optimization, PG-IT2F possesses more suitable phase separation, increased charge transportation, better exciton dissociation, lower bimolecular recombination, and longer charge transfer state lifetime than PY-IT in their polymer solar cells with PM6 as polymer donor. Therefore, the devices based on PM6:PG-IT2F demonstrated a high power conversion efficiency of 17.24%, which is one of the highest efficiency reported for the binary all polymer solar cells to date. This work indicates that the synergistic regulation of small molecule acceptor building block and π-bridge linker plays a key role in designing and developing highly efficient polymer acceptors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

On Stability and Electrochemical Performance of 316 Stainless Steel in Wastewater: Implications for Resource Recovery

Electrochemical nutrient recovery systems rely on stable electrode materials capable of operating in chemically complex wastewater environments. We investigated corrosion resistance and interfacial electrochemical behavior of 316 stainless steel (SS316) in a synthetic wastewater matrix representative of centrate streams, a key knowledge gap in electrochemical phosphorus recovery. A comprehensive suite of electrochemical techniques (chronoamperometry, cyclic voltammetry, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS)) and surface characterization methods (scanning electron microscopy, X-ray diffraction) were employed. Results revealed that wastewater containing typical ionic constituents (such as PO 4 3- , NH 4 + , and divalent cations) exhibited enhanced cathodic activity and the formation of a more stable, protective surface film on SS316 that mitigated chloride-induced corrosion. In contrast, SS316 in the NaCl solution showed significant susceptibility to passive layer breakdown and localized corrosion. Time-resolved EIS further confirmed improved interfacial stability and restricted charge transfer in WW over time, in stark contrast to the progressive passive layer degradation in NaCl. Surface analyses corroborated these findings, showing limited surface attack in WW compared to distinct localized corrosion features in NaCl. These findings indicate that competing ionic species in WW effectively mitigate chloride aggressiveness, enhance SS316 stability, and demonstrate improved electrode longevity and reliability for sustainable wastewater-based electrochemical phosphorus recovery applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗